noise_ydiff: noise_ydiff
In majpark21/TSexploreR: Explorative Tools for Time Series

Description Usage Arguments Value Examples

Provide an estimator of noise level within a group of time-series. First step: compute hierarchical clustering and arrange data according to the clustering order. Second step: compute differences between neighboring rows. Differences can then be raised to a power to accentuate gap between large and small differences. The matrix of differences is then summarized by a list of functions.

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noise_ydiff(x, summary.fun = c("mean", "median", "sd"), power.diff = 1,
  hcl.distance = "euclidean", hcl.linkage = "complete",
  plot.diff = T)

`x`	A numerical matrix. Individuals in rows, measurements in columns.
`summary.fun`	A vector of character, give names of the functions used to summarize the differences. Functions must take a numerical matrix as first input. Functions are applied to the absolute values of differences. Use "colMeans" to get mean difference at each time point.
`power.diff`	numerical, to what power should differences be raised.
`hcl.distance`	character, the method used to compute distances between rows for hierarchical clustering. One of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". See ?stats::dist.
`hcl.linkage`	character, the agglomeration method used in hierarchical clustering. One of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC). See ?stats::hclust.
`plot.diff`	logical, whether to plot the histogram of differences.

A list where each entry is the application of one of summary.fun to the difference matrix.

# Example with normally distributed data (hierarchical clust doesn't really help)
set.seed(7)
x <- rnorm(n = 50)
x <- matrix(x, nrow=5, ncol = 10)
noise_ydiff(x, summary.fun= c("mean", "median", "sd", "colMeans"))


# Example with 3 populations A-C:
# A: oscillates around 10
# B: oscillates around 5
# C: oscillates around 1
n <- 5
len <- 20
express <- expression(t(replicate(n, rnorm(len, mean = 1, sd = 0.1))))
A <- eval(express); A <- A + 9
B <- eval(express); B <- B + 4
C <- eval(express)
ABC <- rbind(A,B,C)
rownames(ABC) <-  make.names(rep(c("A","B","C"), each = 5), unique = T)
# See why hierarchical clust "normalize" the noise estimation:
par(mfrow=c(1,2))
noise_ydiff(ABC)
# Shuffle rows
ABC <- ABC[sample(1:nrow(ABC), replace = FALSE),]
noise_ydiff(ABC) # Recover difference of unshuffled data